Relevant Feature Selection from High-Dimensional Data Using MST Based Clustering

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Relevant Feature Selection from High-Dimensional Data Using MST Based Clustering

Journal Title: International journal of Emerging Trends in Science and Technology - Year 2015, Vol 2, Issue 3

Abstract

Feature selection is the process of identifying a subset of the most useful features that produces compatible results as the original entire set of features. Features provide the information about the data set. In Highdimensional data representation each sample is described by many features. The data sets are typically not task-specific, many features are irrelevant or redundant and should be pruned out or filtered for the purpose of classifying target objects. Given a set of features the feature selection problem is to find a subset of features that “maximizes the learner’s ability to classify patterns”. A graph theoretic clustering algorithm based on boruvka’s algorithm is implemented and experimentally evaluated in this paper. The proposed algorithm works in two steps. In the first step, features are divided into clusters by using graphtheoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features. All the representative features from different clusters form the final feature subset. After finding feature subset accuracy of a classifier, time required for classification and proportion of features selected can be calculated

Authors and Affiliations

Yaswanth Kumar Alapati

Keywords

EP ID EP221601
DOI -
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